OSCR

Integrating multiple sensory modalities during dyadic interactions drives self-other distinction at the behavioral and electrocortical level.

Code ↔ Paper

4 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 4 matches
  1. [1] § STAR★Methods › Quantification and statistical analysis › EEG source reconstruction ↔ EBS_source_analysis_raw.m, lines 39–92 · score 0.96 · volume conduction model, 125–175 ms, 200–250 ms, 25–75 ms, 300–350 ms, 75–125 ms
  2. [2] § STAR★Methods › Method details › Electroencephalogram recording and processing ↔ EBS_EEGLAB_FT_pipeline_raw.m, lines 48–85 · score 0.73 · high pass filter, low pass filter, EEGLAB, trace, ICA, Component
  3. [3] § STAR★Methods › Method details › Electroencephalogram recording and processing ↔ EBS_EEGLAB_FT_pipeline_raw.m, lines 177–258 · score 0.72 · ft_artifact_jump, median filter, segments, neighbors, weighted, FieldTrip
  4. [4] § STAR★Methods › Quantification and statistical analysis › EEG multivariate analysis › Multiclass decoding of the interaction ↔ EBS_MVPA_multiclass_overtime_raw.m, lines 97–170 · score 0.54 · cross validation, fold, multiclass, training, LDA, decoding

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

MATLAB · 258 lines · 10 KB · no license · 2 matches

  1. %% EBS - SCRIPT PREPROCESS DATA IN EEGLAB + FT
  3. % Read the data in FT
  5. cfg = [];
  6. cfg.dataset = filename_base; % name of your dataset
  7. % cfg.channel = {'all', '-129', '-130', '-131', '-132', '-133', '-134', '-135', '-136', '-137', '-138', '-139', '-140', '-141', '-142', '-143', '-144'};
  8. data_ft_BASE = ft_preprocessing(cfg);
  10. cfg = [];
  11. cfg.dataset = filename_uni; % name of your dataset
  12. % cfg.channel = {'all', '-129', '-130', '-131', '-132', '-133', '-134', '-135', '-136', '-137', '-138', '-139', '-140', '-141', '-142', '-143', '-144'};
  13. data_ft_uni = ft_preprocessing(cfg);
  15. % cfg = [];
  16. % cfg.dataset = filename_uni_vis; % name of your dataset
  17. % % cfg.channel = {'all', '-129', '-130', '-131', '-132', '-133', '-134', '-135', '-136', '-137', '-138', '-139', '-140', '-141', '-142', '-143', '-144'};
  18. % data_ft_uniVIS = ft_preprocessing(cfg);
  20. cfg = [];
  21. cfg.dataset = filename_P; % name of your dataset
  22. % cfg.channel = {'all', '-129', '-130', '-131', '-132', '-133', '-134', '-135', '-136', '-137', '-138', '-139', '-140', '-141', '-142', '-143', '-144'};
  23. data_ft_PROPRIO = ft_preprocessing(cfg);
  25. cfg = [];
  26. cfg.dataset = filename_V; % name of your dataset
  27. % cfg.channel = {'all', '-129', '-130', '-131', '-132', '-133', '-134', '-135', '-136', '-137', '-138', '-139', '-140', '-141', '-142', '-143', '-144'};
  28. data_ft_VISION = ft_preprocessing(cfg);
  30. cfg = [];
  31. cfg.dataset = filename_PV; % name of your dataset
  32. % cfg.channel = {'all', '-129', '-130', '-131', '-132', '-133', '-134', '-135', '-136', '-137', '-138', '-139', '-140', '-141', '-142', '-143', '-144'};
  33. data_ft_PROPRIOVISION = ft_preprocessing(cfg);
  35. % save .set data to load in EEGLAB
  37. data_eeglab_BASE = fieldtrip2eeglab(data_ft_BASE.hdr, cat(3,data_ft_BASE.trial{:}));
  38. pop_saveset(data_eeglab_BASE, 'filename', 'data_eeglab_BASE.set')
  39. data_eeglab_uni = fieldtrip2eeglab(data_ft_uni.hdr, cat(3,data_ft_uni.trial{:}));
  40. pop_saveset(data_eeglab_uni, 'filename', 'data_eeglab_uni.set')
  41. data_eeglab_P = fieldtrip2eeglab(data_ft_PROPRIO.hdr, cat(3,data_ft_PROPRIO.trial{:}));
  42. pop_saveset(data_eeglab_P, 'filename', 'data_eeglab_PROPRIO.set')
  43. data_eeglab_V = fieldtrip2eeglab(data_ft_VISION.hdr,cat(3,data_ft_VISION.trial{:}));
  44. pop_saveset(data_eeglab_V, 'filename', 'data_eeglab_VISION.set')
  45. data_eeglab_PV = fieldtrip2eeglab(data_ft_PROPRIOVISION.hdr,cat(3,data_ft_PROPRIOVISION.trial{:}));
  46. pop_saveset(data_eeglab_PV, 'filename', 'data_eeglab_PROPRIOVISION.set')
  48. %% USE EEGLAB GUI TO LOAD FILE AND EVENTS, USE CleanRaw and MARA plugins, SAVE AS .set
  50. % 1. Import .set file and add events (txt. file) for each recording trace
  51. % 2. Append all (BASE - UNI_TAC - UNI_VIS - PV - P - V)
  52. % '..._all_raw.set'
  53. % 3. Remove reference channels, add channel locations
  54. % 4. save
  55. save(fullfile(EEG.filepath, EEG.filename), '-v7.3', '-mat', '-struct', 'EEG');
  57. % 5. HIGH-PASS filter at 0.1 Hz as cut-off freq (0.2 Hz hp limit)
  58. % '..._all_raw_hpfilt.set'
  60. % 5.1. LOW-PASS filter at 48 Hz as cut-off frequency (43 Hz lp limit)
  61. % '..._all_raw_filt.set'
  63. % 6. Find bad electrodes running Cleanraw routine
  64. % '..._cleanraw080.set' + save
  65. save(fullfile(EEG.filepath, EEG.filename), '-v7.3', '-mat', '-struct', 'EEG');
  67. % 6.1 Epoch cleanraw data (from -1.2 to 1.2) -> '...cleanraw_epoched'
  69. % 6.2. Decompose by ICA (using cleanraw_hpfilt_epoched.set)
  71. % !!!SAVE!!! '...cleanraw_ICA.set'
  72. save(fullfile(EEG.filepath, EEG.filename), '-v7.3', '-mat', '-struct', 'EEG');
  74. % 7. Classify ICA components and remove bad components after checking
  76. % '...cleanraw_ICclean.set'
  78. % 9.Find removed electrodes after cleanraw:
  79. removed_ch = find(EEG.etc.clean_channel_mask == 0); % List of removed channels by CleanRaw
  81. % 10. save dataset "...preprocessed"
  83. % 11. save event struct as EEG.event + save in -v7.3:
  84. EEG_event = EEG.event;
  85. save('path\EEG_event', 'EEG_event')
  87. %% COMBINE BEHAVIORAL AND EEG DATA
  89. % Read the data in FT after EEGLAB
  91. cfg = [];
  92. cfg.dataset = 'data.set'; % name of your dataset
  93. % cfg.channel = {'all', '-A1_left', '-A2_right_REF'};
  94. data_all_raw = ft_preprocessing(cfg);
  96. % cfg = [];
  97. % cfg.continuous = 'yes';
  98. % cfg.viewmode = 'vertical';
  99. % cfg.ploteventlabels = 'type=value';
  100. % ft_databrowser(cfg, data_all_raw);
  102. % add trialinfo
  103. load('EEG_event.mat');
  105. % CHECK TRIGGERS FROM EPRIME CORRESPOND TO THE ONES OF THE EEG
  106. A = [EEG_event.trigger]';
  107. B = behavS05_s(:,4);
  108. C = A-B
  110. data_all_raw.trialinfo = struct2table(EEG_event);
  111. data_all_raw.trialinfo = data_all_raw.trialinfo{:,3};
  113. % cfg = [];
  114. % cfg.continuous = 'yes';
  115. % cfg.viewmode = 'vertical';
  116. % cfg.ploteventlabels = 'type=value';
  117. % ft_databrowser(cfg, data_all_raw_reref);
  119. % remove behav bad trials
  121. cfg = [];
  122. cfg.trials = behav_outlierfree(:,5)'; % specify 1xN vector with trials of behavOutlierFree PLUS N of unisensory trials
  123. data_all_raw_noout = ft_redefinetrial(cfg, data_all_raw);
  125. % add ordinal values to trial info to help matching
  127. data_all_raw_noout.trialinfo(:,2) = 1:1:size(data_all_raw_noout.trialinfo(:,1));
  129. % Interpolate bad electrodes (if present)
  131. elec = ft_read_sens('standard_1005.elc');
  133. cfg_neighb = [];
  134. cfg_neighb.method = 'distance'; % just for interpolating Fp1
  135. cfg_neighb.neighbourdist = .15;
  136. cfg.senstype = 'EEG';
  137. cfg_neighb.layout = 'EEG1005.lay';
  138. cfg_neighb.feedback = 'no';
  139. neighbours = ft_prepare_neighbours(cfg_neighb);
  141. cfg = [];
  142. cfg.method = 'weighted';
  143. cfg.missingchannel = {'CP5', 'CP4'}; % TP10 if needed
  144. % cfg.badchannel = {'AF7'};
  145. cfg.neighbours = neighbours;
  146. cfg.trials = 'all';
  147. cfg.elec = elec;
  148. data_all_raw_noout_int = ft_channelrepair(cfg, data_all_raw_noout);
  150. cfg_neighb = [];
  151. cfg_neighb.method = 'triangulation'; % just for interpolating Fp1
  152. % cfg_neighb.neighbourdist = .15;
  153. cfg.senstype = 'EEG';
  154. cfg_neighb.layout = 'EEG1005.lay';
  155. cfg_neighb.feedback = 'no';
  156. neighbours = ft_prepare_neighbours(cfg_neighb);
  158. cfg = [];
  159. cfg.method = 'weighted';
  160. % cfg.missingchannel = {'CP5', 'CP4'}; % TP10 if needed
  161. cfg.badchannel = {'TPP10h'};
  162. cfg.neighbours = neighbours;
  163. cfg.trials = 'all';
  164. cfg.elec = elec;
  165. data_all_raw_noout_int = ft_channelrepair(cfg, data_all_raw_noout_int);
  167. % rereferencing and baseline correction
  169. cfg = [];
  170. cfg.reref = 'yes';
  171. cfg.refmethod = 'avg';
  172. cfg.refchannel = 'all';
  173. % cfg.demean = 'yes';
  174. % cfg.baselinewindow = [-.1 0];
  175. data_all_raw_noout_final = ft_preprocessing(cfg, data_all_raw_noout_int);
  177. %% inspect by eye
  179. % Step 1: Configure jump artifact detection
  180. cfg = [];
  181. cfg.dataset = []; % Leave empty since data is already loaded
  182. cfg.artfctdef.jump.channel = 'EEG'; % You can use 'all' or specify EEG channels
  183. cfg.artfctdef.jump.medianfilter = 'yes';
  184. cfg.artfctdef.jump.medianfiltord = 9; % Order of median filter
  185. cfg.artfctdef.jump.absdiff = 'yes';
  186. cfg.artfctdef.jump.cutoff = 20; % Try starting from 20, adjust based on data
  187. cfg.artfctdef.jump.trlpadding = 0;
  188. cfg.artfctdef.jump.fltpadding = 0;
  189. cfg.artfctdef.jump.artpadding = 0.1;
  190. cfg.artfctdef.jump.interactive = 'yes';
  192. % Step 2: Detect jump artifacts
  193. [cfg, artifact_jump] = ft_artifact_jump(cfg, data_all_raw_noout_final);
  195. % Step 3: Reject trials containing jump artifacts
  196. cfg = [];
  197. cfg.artfctdef.reject = 'complete'; % 'complete' removes entire trial, 'partial' can mark segment
  198. cfg.artfctdef.jump.artifact = artifact_jump;
  199. data_all_visart = ft_rejectartifact(cfg, data_all_raw_noout_final);
  201. % cfg = [];
  202. % cfg.method = 'trial';
  203. % % cfg.latency = [-1 1]; % part of the trial you are interested in viewing (the default one is the whole length)
  204. % cfg.preproc.bpfilter = 'yes'
  205. % cfg.preproc.bpfreq = [.5 30]
  206. % % cfg.preproc.bpfiltord = 8
  207. % cfg.preproc.bpfilttype = 'but'
  208. % data_all_visart = ft_rejectvisual(cfg, data_all_raw_noout_final);
  210. % cfg1 = [];
  211. % cfg1.method = 'weighted';
  212. % % cfg1.missingchannel = {'TPP8h', 'CP6'}; % AFF9h if needed
  213. % cfg1.badchannel = {'TP9'};
  214. % cfg1.neighbours = neighbours;
  215. % cfg1.trials = 'all';
  216. % cfg1.elec = elec;
  217. % data_all_visart = ft_channelrepair(cfg1, data_all_visart);
  219. save('path', 'data_all_visart','-v7.3');
  221. rejected = data_all_raw_noout_final.trialinfo((find(~ismember(data_all_raw_noout_final.trialinfo(:,2),data_all_visart.trialinfo(:,2)))),2);
  222. save('path', 'rejected')
  224. % cfg = [];
  225. % cfg.method = 'trial';
  226. % cfg.latency = [-1 1]; % part of the trial you are interested in viewing (the default one is the whole length)
  227. % cfg.preproc.bpfilter = 'yes'
  228. % cfg.preproc.bpfreq = [.5 30]
  229. % % cfg.preproc.bpfiltord = 8
  230. % cfg.preproc.bpfilttype = 'but'
  231. % data_all_visart = ft_rejectvisual(cfg, data_all_raw_noout_final);
  232. %
  233. % save('D:\res_backup\entangled_body_schema\data_MAIN\preprocessed\S05_data_all_preproc_def', 'data_all_visart')
  234. %
  235. % rejected = data_all_raw_noout.trialinfo((find(~ismember(data_all_raw_noout.trialinfo(:,2),data_all_visart.trialinfo(:,2)))),2);
  236. % save('D:\res_backup\entangled_body_schema\data_MAIN\preprocessed\S05_rejectedEEG_trls', 'rejected')
  238. % remove EEG artifacts from behavioral data (for further behav-EEG analyses)
  240. % rejected1 = rejected;
  241. % rejected1(rejected1<160) = []; % remove trials from unisensory % baseline, not included in behav file
  242. % rejected1 = rejected1-160; % get back to the indices with no unisensory and base trials
  243. % S05_BehavFinal_all = behav_outlierfree;
  244. %
  245. % c = ismember(S05_BehavFinal_all(:,5), rejected1); % find rejected trials
  246. % indexes = find(c);
  247. % S05_BehavFinal_all([indexes],:) = []; % remove from behav file
  248. BehavFinal_all = behav_outlierfree;
  249. BehavFinal_all([rejected],:) = []; % remove from behav file
  251. % check behav and EEG match one last time
  252. A = data_all_visart.trialinfo(:,1);
  253. B = BehavFinal_all(:,4);
  254. C = A-B
  256. save('path', 'BehavFinal_all');
  258. clear

EBS_EEGLAB_FT_pipeline_raw.m at commit 811566d, no license · at the source

Overview

Authors: Ugo Giulio Pesci1,2, Giovanna Cuomo1,2, Vanessa Era1,2, Matteo Candidi1,2
ORCID iDs: Ugo Giulio Pesci, Vanessa Era, Matteo Candidi
  1. Department of Psychology, Sapienza University, Rome, Italy
  2. IRCCS Fondazione Santa Lucia, Rome, Italy
Institutions: Fondazione Santa Lucia (Italy); Sapienza University of Rome (Italy)
Journal: iScience, volume 29, issue 5, article 115708
Dates: received 6 September 2025; accepted 8 April 2026; published online 10 April 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1016/j.isci.2026.115708 · PMID 42111198 · PMCID PMC13156570 · OpenAlex W4412831649
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.08.01.668120
Status: code verified
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Evoked potentials, Physiology & signal measures
Keywords: neuroscience, sensory neuroscience
Topic: Multisensory perception and integration (Experimental and Cognitive Psychology, Psychology), according to OpenAlex
Funding: University of Rome La Sapienza (RM1221816C827130, RG123188B4631694, MA22117A8A97CD42); Ministry of Health (GR-2021-12372923)
Citations: not cited yet (Europe PMC); 60 references in the paper
Research resources: MATLAB R2022b RRID:SCR_001622, R version 3.5.1 RRID:SCR_001905, E-prime 2.0 RRID:SCR_009567, MorePower 6.0.4 RRID:SCR_024210

Abstract

Interpersonal motor interactions represent ecologically relevant dynamic contexts for studying behavioral and neural effects of active multisensory experiences. They offer the possibility to study cross-modal multisensory integration mechanisms and to test whether interpersonal interactions impact interpersonal cross-modal processing. Here we explored whether being engaged in interpersonal interactions that require the integration of different sensorimotor signals modulates interpersonal cross-modal integration after the interaction. In detail, we investigated whether engaging individuals in dyadic activities that utilized either single or combined sensory modalities would impact the behavioral and electrocortical markers associated with interpersonal cross-modal integration. We show that interactions requiring the integration of multiple sensory modalities lead to higher interpersonal differentiation resulting in reduced interpersonal cross-modal integration. Further, the neural patterns elicited by interpersonal visuo-tactile stimuli presented after interpersonal interactions that involved multiple sensory modalities were easier to recognize by a neural classifier. These findings suggest new avenues for sensorimotor approaches in social neuroscience, emphasizing the malleability of self-other representations based on the nature of interpersonal interactions.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 4 matches between paragraphs and lines of code.

ugopesci/Integrating-multiple-sensory-modalities-during-dyadic-interactions

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 811566db1e2f1b23de5da165c9ff79d7d400e0cf, 20 November 2025
Languages: MATLAB (3)
Size: 3 files, 3 scripts
Software Heritage: not archived
Found in: “Data and code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FieldTrip (3 files), EEGLAB (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
3 files
At the source: github.com/ugopesci/Integrating-multiple-sensory-modalities-during-dyadic-interactions

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 3 scripts, each with its path and the digest of its content;
  • 4 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data and code availability

All data reported in this study will be shared by the lead contacts upon request.

This study does not report original code, and analysis scripts are provided on an online repository at: https://github.com/ugopesci/Integrating-multiple-sensory-modalities-during-dyadic-interactions.

Any additional information required to re-analyze the data reported in this study is available from the lead contact upon request.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 2 keywords, 2 funders, 55 references, 4 RRIDs.

Cite

This paper

Pesci, U. G., Cuomo, G., Era, V., & Candidi, M. (2026). Integrating multiple sensory modalities during dyadic interactions drives self-other distinction at the behavioral and electrocortical level. iScience, 29(5), 115708. https://doi.org/10.1016/j.isci.2026.115708

BibTeX

@article{pesci2026integrating,
author = {Pesci, Ugo Giulio and Cuomo, Giovanna and Era, Vanessa and Candidi, Matteo},
title = {{Integrating multiple sensory modalities during dyadic interactions drives self-other distinction at the behavioral and electrocortical level}},
journal = {iScience},
year = {2026},
month = apr,
volume = {29},
number = {5},
pages = {115708},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.115708},
url = {https://doi.org/10.1016/j.isci.2026.115708},
pmid = {42111198},
pmcid = {PMC13156570}
}

RIS

TY - JOUR
AU - Pesci, Ugo Giulio
AU - Cuomo, Giovanna
AU - Era, Vanessa
AU - Candidi, Matteo
TI - Integrating multiple sensory modalities during dyadic interactions drives self-other distinction at the behavioral and electrocortical level
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/04/10
VL - 29
IS - 5
SP - 115708
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.115708
UR - https://doi.org/10.1016/j.isci.2026.115708
LA - en
ER -

CSL-JSON

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"id": "10.1016/j.isci.2026.115708",
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"given": "Ugo Giulio"
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"DOI": "10.1016/j.isci.2026.115708",
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"PMCID": "PMC13156570",
"ISSN": "2589-0042",
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"URL": "https://doi.org/10.1016/j.isci.2026.115708",
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"issued": {
"date-parts": [
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2026,
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